Fuzzy integral in multicriteria decision making
Fuzzy Sets and Systems - Special issue on fuzzy information processing
k-order additive discrete fuzzy measures and their representation
Fuzzy Sets and Systems - Special issue on fuzzy measures and integrals
Optimization issues for fuzzy measures
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - A special issue on fuzzy measures
Fuzzy Measure Theory
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
The Choquet integral for the aggregation of interval scales in multicriteria decision making
Fuzzy Sets and Systems - Special issue: Preference modelling and applications
Identification of fuzzy measures from sample data with genetic algorithms
Computers and Operations Research
Fuzzy Optimization and Decision Making
An axiomatic approach to the definition of the entropy of a discrete Choquet capacity
Information Sciences: an International Journal
A graphical interpretation of the Choquet integral
IEEE Transactions on Fuzzy Systems
An axiomatic approach of the discrete Choquet integral as a tool to aggregate interacting criteria
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Intuitionistic fuzzy-valued Choquet integral and its application in multicriteria decision making
Information Sciences: an International Journal
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Fuzzy measures can flexibly describe the relative importance of decision criterion as well as their interactions in multicriteria decision making. Based on the diamond pairwise comparison, a new identification method of 2-order additive fuzzy measure is proposed. The relative weight and the interaction degree can be obtained simultaneously for every pair of criteria in the diamond pairwise comparison. The Choquet integral-based equivalent alternative curve can help the decision maker estimate the interaction degrees between criteria. The overall importance of each criterion is obtained by the maximum eigenvector method of AHP. According to the maximum fuzzy measure entropy principal, a nonlinear programming is constructed to identify the interaction indices among criteria. Finally, an illustrative example shows the feasibility and validity of the proposed identification method.